Task collaborative offloading scheme in vehicle multi-access edge computing network

Guanhua QIAO; Supeng LENG; Hao LIU; Kaisheng HUANG; Fan WU

doi:10.11959/j.issn.2096-3750.2019.00089

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Task collaborative offloading scheme in vehicle multi-access edge computing network

Theory and Technology | 更新时间：2024-06-05

- Task collaborative offloading scheme in vehicle multi-access edge computing network
- Chinese Journal on Internet of Things Vol. 3, Issue 1, Pages: 51-59(2019)
- 作者机构：
  
  1. 电子科技大学信息与通信工程学院，四川成都 611731
  2. 北京市交通信息中心，北京 100073
  3. 清华大学汽车安全与节能国家重点实验室，北京 100084
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-3750.2019.00089
  CLC： TN929.53
- Published：30 March 2019，
  
  Published Online：2019-03，
- 稿件说明：
移动端阅览
GUANHUA QIAO, SUPENG LENG, HAO LIU, et al. Task collaborative offloading scheme in vehicle multi-access edge computing network. [J]. Chinese journal on internet of things, 2019, 3(1): 51-59.
DOI：

GUANHUA QIAO, SUPENG LENG, HAO LIU, et al. Task collaborative offloading scheme in vehicle multi-access edge computing network. [J]. Chinese journal on internet of things, 2019, 3(1): 51-59. DOI： 10.11959/j.issn.2096-3750.2019.00089.

摘要

为了解决传统移动边缘计算网络无法很好地支持车辆的高速移动性和动态网络拓扑，设计了车辆多址接入边缘计算网络，实现路边单元和智能车辆的协同计算迁移。在该网络架构下，提出了多址接入模式选择和任务分配的联合优化问题，旨在最大化系统的长期收益，同时满足多样化的车联网应用需求，兼顾系统的能量消耗。针对该复杂的联合优化问题，设计了基于深度增强学习的多址接入协同计算迁移策略，该策略能够很好地克服传统Q-learning算法因网络规模增加带来的维度灾难挑战。仿真结果验证了所提算法具有良好的计算性能。

Abstract

In order to solve the problem that traditional mobile edge computing network can’t be straightforwardly applied to the Internet of vehicles (IoV) due to high speed mobility and dynamic network topology

a vehicular edge multi-access computing network (VE-MACN) was introduced to realize collaborative computing offloading between roadside units and smart vehicles.In this context

the collaborative computation offloading was formulated as a joint multi-access model selection and task assignment problem to realize the good balance between long-term system utility

diverse needs of IoV applications and energy consumption.Considering the complex joint optimization problem

a deep reinforcement learning-based collaborative computing offloading scheme was designed to overcome the curse of dimensionality for Q-learning algorithm.The simulation results demonstrate that the feasibility and effectiveness of proposed offloading scheme.

关键词

移动边缘计算多址接入技术车联网计算迁移深度增强学习

Keywords

mobile edge computingmulti-access technologyInternet of vehicles (IoV)computation offloadingdeep reinforcement learning

references

WOLLSCHLAEGER M, SAUTER T, JASPERNEITE J.The future of industrial communication:automation networks in the era of the Internet of things and industry 4 . 0[J]. IEEE Industrial Electronics Magazine, 2017,11(1): 17-27.

ABBAS N, ZHANG Y, TAHERKORDI A ,et al. Mobile edge computing:a survey[J]. IEEE Internet of Things, 2018,5(1): 450-465.

MAO Y Y, YOU C S, ZHANG J ,et al. A survey on mobile edge computing:the communication perspective[J]. IEEE Communication Survey ＆ Tutorials, 2017,19(4): 2322-2358.

MACH P, BECVAR Z . Mobile edge computing:a survey on architecture and computation offloading[J]. IEEE Communication Survey ＆Tutorials, 2017,19(3): 1628-1656.

ETSI ISGs . Mobile edge computing:a key technology towards 5G[S]. White Paper, 2015: 1-16.

ZHENG K, ZHENG Q, CHATZIMISIONS P ,et al. Heterogeneous vehicular networking:a survey on architecture,challenges and solutions[J]. IEEE Communication Surveys ＆ Tutorials, 2015,17(4): 2377-2396.

ZHANG K, MAO Y M, LENG S P ,et al. Energy-efficient offloading for mobile edge computing in 5G heterogeneous networks[J]. IEEE Access, 2016(4): 5896-5907.

ZHANG K, MAO Y M, LENG S P ,et al. Mobile-edge computing for vehicular networks:a promising network paradigm with predictive offloading[J]. IEEE Vehicle Technology Magazine, 2017,12(2): 36-44.

HOU X S, LI Y, CHEN M ,et al. Vehicular fog computing:a viewpoint of vehicles as the infrastructures[J]. IEEE Transactions on Vehicular Technology, 2016,65(6): 3860-3873.

HU Q T, WU C, ZHAO X B ,et al. Vehicular multi-access edge computing with licensed sub-6 GHz,IEEE 802.11p and mmWave[J]. IEEE Access, 2017(5): 1995-2004.

GUO H Z, LIU J J . Collaborative computation offloading for multiaccess edge computing over fiber-wireless networks[J]. IEEE Transactions on Vehicular Technology, 2017,12(2): 4514-4526.

MNIH V, KAVUKCUOGLU K, SILVER D ,et al. Human-level control through deep reinforcement learning[J]. Nature, 2015,518(7540): 529-533.

KASHIHARA K, . Deep Q-learning for traffic simulation in autonomous driving at a highway junction[C]// IEEE International Conference on Systems,Man and Cybernetics (SMC). IEEE, 2017: 984-988.

HE Y, ZHAO N, YIN H X . Integrated networking,caching and computing for connected vehicles:a deep reinforcement learning approach[J]. IEEE Transactions on Vehicular Technology, 2017,67(1): 44-55.

GUAN K, ZHONG Z D, AI B ,et al. Propagation measurement and modeling of crossing bridge on high-speed railway at 930 MHz[J]. IEEE Transactions on Vehicular Technology, 2014,63(2): 508-514.

VALCARCE A, ZHANG J.Empirical indoor-to-outdoor propagation model for residential areas at 0.9-3 . 5 GHz[J]. IEEE Antennas and Wireless Propagation Letters, 2010(9): 682-685.

MIETTINEN A P, NURMINEN J K . Energy efficiency of mobile clients in cloud computing[C]// USENIX Conference on Hot Topics in Cloud Computing. USENIX Association, 2010: 1-7.

MAO Y Y, ZHANG J, LETAIEF K B . Dynamic computation offloading for mobile-edge computing with energy harvesting devices[J]. IEEE Journal on Selected Areas in Communications, 2016,34(12): 3590-3605.

ZHANG W W, WEN Y G, GUAN K ,et al. Energy-optimal mobile cloud computing under stochastic wireless channel[J]. IEEE Transactions on Wireless Communications, 2013,12(9): 4569-4581.

RAPPAPORT T S . Wireless communication:principles and practice[M]. New Jersey: Prentice Hall PRT, 1996.

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